41–50 of 207 posts
I can't name any names for obvious reasons or give you more hints about what industry this company is in but I just did DD on a very impressive outfit that ran their entire company on Google's cloud platform, it held about 500T of data and held up amazingly well under load. I was super impressed with how they had set this all up and they were extremely well aware of all the limitations and do's and dont's of that par…
I can't name any names for obvious reasons or give you more hints about what industry this company is in but I just did DD on a very impressive outfit that ran their entire company on Google's cloud platform, it held about 500T of data and held up amazingly well under load. I was super impressed with how they had set this all up and they were extremely well aware of all the limitations and do's and dont's of that par…
DD?
I can't name any names for obvious reasons or give you more hints about what industry this company is in but I just did DD on a very impressive outfit that ran their entire company on Google's cloud platform, it held about 500T of data and held up amazingly well under load. I was super impressed with how they had set this all up and they were extremely well aware of all the limitations and do's and dont's of that par…
DD?
I can't name any names for obvious reasons or give you more hints about what industry this company is in but I just did DD on a very impressive outfit that ran their entire company on Google's cloud platform, it held about 500T of data and held up amazingly well under load. I was super impressed with how they had set this all up and they were extremely well aware of all the limitations and do's and dont's of that par…
DD?
I have spent the last year and a half building a completely serverless production service on Lambda, API Gateway, and DynamoDB (along with the standard auxiliary services like CW, SNS, Route53, S3, CF, X-Ray, etc.). It was a lot of work establishing new patterns for many of the operational aspects, particularly custom CW metrics and A/B deployments with Lambda traffic shifting, but in the end everything is set up nic…
I have spent the last year and a half building a completely serverless production service on Lambda, API Gateway, and DynamoDB (along with the standard auxiliary services like CW, SNS, Route53, S3, CF, X-Ray, etc.). It was a lot of work establishing new patterns for many of the operational aspects, particularly custom CW metrics and A/B deployments with Lambda traffic shifting, but in the end everything is set up nic…
Does Lambda have a good version control or CI workflow? My biggest question is how to develop severless functions with a team of developers.
It's also possible to use API Gateway canary deployments and dedicated "preprod" stages as well - the benefit being you can, in theory, use traffic shifting lifecycle hooks to deploy a new version completely isolated from customer traffic and test it before incrementally rolling out your main A/B deployment. I created an experimental proof-of-concept for this but haven't had the time to flesh it out for production usage, but would very much like to at some point.
tl;dr The answer to your question is yes, there is a good story around CI with Lambda using the AWS ecosystem. However Lambda alone is not something that should be relied on for long-term or definitive versioning on its own.
Edit: thinking about your question more, it seems you are assuming that individual developers will directly edit Lambda function code in the console and you want to track versions or trigger deployments based on that activity. You absolutely should not ever be editing Lambda code manually directly in the console outside of one-off experiments/prototypes that are completely unrelated to dev/test/production. Always keep your Lambda function code under source control and deploy using zips uploaded to S3 and CloudFormation (the serverless framework[1] provides good tooling for this, though we don't use it - instead we use SAM and our own internal tools).
Example: there's a website for all the weather radar stations in Canada that lets you see the last two hours of 10-minute radar snapshots. I wanted a dump of them to try some ML algorithms, but even after requests and emails there simply wasn't one.
So I set up a lambda to run every hour, load the website for all 31 weather stations and save all 6 images from the last hour to S3.
It took me an hour to setup. I've never gotten around to making that ML project, but lambda has kept on chugging away for me, GBs of data saved away. The only real cost is the s3 storage, still under $2/month.
Serverless is brilliant for little things like that.